feat : adds metadata filtering

embeddingstores/pgvector-springai/src/main/java/com/learning/ai/config/SwaggerConfig.java

@@ -1,12 +1,10 @@
-package com.learning.ai.config;
-
-import io.swagger.v3.oas.annotations.OpenAPIDefinition;
-import io.swagger.v3.oas.annotations.info.Info;
-import io.swagger.v3.oas.annotations.servers.Server;
-import org.springframework.context.annotation.Configuration;
-
-@Configuration(proxyBeanMethods = false)
-@OpenAPIDefinition(
-        info = @Info(title = "pgvector-springai", version = "v1.0.0"),
-        servers = @Server(url = "/"))
-public class SwaggerConfig {}
+package com.learning.ai.config;
+
+import io.swagger.v3.oas.annotations.OpenAPIDefinition;
+import io.swagger.v3.oas.annotations.info.Info;
+import io.swagger.v3.oas.annotations.servers.Server;
+import org.springframework.context.annotation.Configuration;
+
+@Configuration(proxyBeanMethods = false)
+@OpenAPIDefinition(info = @Info(title = "pgvector-springai", version = "v1.0.0"), servers = @Server(url = "/"))
+public class SwaggerConfig {}

embeddingstores/pgvector-springai/src/main/java/com/learning/ai/controller/QueryController.java

@@ -18,7 +18,7 @@ public QueryController(PgVectorStoreService pgVectorStoreService) {
     }
 
     @GetMapping("/query")
-    AIChatResponse queryEmbeddedStore(@RequestParam String question) {
-        return pgVectorStoreService.queryEmbeddingStore(question);
+    AIChatResponse queryEmbeddedStore(@RequestParam String question, @RequestParam(required = false) Integer userId) {
+        return pgVectorStoreService.queryEmbeddingStore(question, userId);
     }
 }

embeddingstores/pgvector-springai/src/main/java/com/learning/ai/service/PgVectorStoreService.java

@@ -2,6 +2,7 @@
 
 import com.learning.ai.model.response.AIChatResponse;
 import java.util.List;
+import java.util.Map;
 import java.util.stream.Collectors;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
@@ -23,14 +24,19 @@ public PgVectorStoreService(VectorStore vectorStore) {
 
     public void storeEmbeddings() {
         // Store embeddings
-        List<Document> documents =
-                List.of(new Document("I like football."), new Document("The weather is good today."));
+        List<Document> documents = List.of(
+                new Document("I like football.", Map.of("userId", 1)),
+                new Document("I like cricket.", Map.of("userId", 2)),
+                new Document("The weather is good today."));
         vectorStore.add(documents);
     }
 
-    public AIChatResponse queryEmbeddingStore(String question) {
+    public AIChatResponse queryEmbeddingStore(String question, Integer userId) {
         // Retrieve embeddings
         SearchRequest query = SearchRequest.query(question).withTopK(1);
+        if (userId != null) {
+            query.withFilterExpression("userId == " + userId);
+        }
         List<Document> similarDocuments = vectorStore.similaritySearch(query);
         String relevantData =
                 similarDocuments.stream().map(Document::getContent).collect(Collectors.joining(System.lineSeparator()));

embeddingstores/pgvector-springai/src/test/java/com/learning/ai/controller/QueryControllerTest.java

@@ -28,10 +28,32 @@ void setUp() {
     @Test
     void queryEmbeddedStore() {
         given().param("question", "What is your favourite sport")
+                .param("userId", 1)
                 .when()
                 .get("/api/ai/query")
                 .then()
                 .statusCode(200)
                 .body("answer", equalTo("I like football."));
     }
+
+    @Test
+    void queryEmbeddedStoreWithMetadata() {
+        given().param("question", "What is your favourite sport")
+                .param("userId", 2)
+                .when()
+                .get("/api/ai/query")
+                .then()
+                .statusCode(200)
+                .body("answer", equalTo("I like cricket."));
+    }
+
+    @Test
+    void queryEmbeddedStoreWithOutMetadata() {
+        given().param("question", "What is weather today")
+                .when()
+                .get("/api/ai/query")
+                .then()
+                .statusCode(200)
+                .body("answer", equalTo("The weather is good today."));
+    }
 }